{"id":"W2768004508","doi":"10.1016/j.actbio.2017.11.024","title":"Injectable nanocomposite cryogels for versatile protein drug delivery","year":2017,"lang":"en","type":"article","venue":"Acta Biomaterialia","topic":"Hydrogels: synthesis, properties, applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":168,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; Hansjörg Wyss Institute for Biologically Inspired Engineering, Harvard University; Howard Hughes Medical Institute; National Institutes of Health; National Science Foundation","keywords":"Self-healing hydrogels; Drug delivery; Materials science; Protein adsorption; Nanocomposite; Nanotechnology; Controlled release; Nanoparticle; Drug carrier; Adsorption; Chemical engineering; Chemistry; Polymer; Polymer chemistry; Composite material; Organic chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002053156,0.0001972992,0.000183043,0.0000329611,0.0005545943,0.0002607801,0.0006486063,0.0001504085,0.00005783897],"category_scores_gemma":[0.00007467491,0.0001857778,0.00009658151,0.00002287433,0.000138526,0.0000207495,0.0002678317,0.0000226045,0.00006256087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002534785,"about_ca_system_score_gemma":0.0000828829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005875955,"about_ca_topic_score_gemma":0.0000740117,"domain_scores_codex":[0.9988436,0.00004072626,0.0002324117,0.000429319,0.000095111,0.0003588614],"domain_scores_gemma":[0.9985526,0.000004723358,0.0001989839,0.00106332,0.00009120236,0.00008915934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001533827,0.00005135933,0.0001130996,0.00003924394,0.00006119977,5.893136e-7,0.00005256923,2.34583e-7,0.9914057,0.00002379417,0.00737393,0.0007248839],"study_design_scores_gemma":[0.000425165,0.00007330751,0.0003817945,0.00002161318,0.00002302633,0.000003255166,0.00001424874,0.00001936865,0.8334196,0.00007847248,0.1653259,0.0002143207],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973171,0.00008848742,0.0001091981,0.0004873164,0.0003668517,0.0009377862,0.0002309068,0.00004034482,0.0004220555],"genre_scores_gemma":[0.9942439,0.00001576541,0.002135629,0.00008643419,0.000356207,0.0007013176,0.0002065436,0.00004794247,0.002206309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1579862,"threshold_uncertainty_score":0.7575799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01622983690341256,"score_gpt":0.2499602964210255,"score_spread":0.233730459517613,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}